Overview

Dataset statistics

Number of variables17
Number of observations40000
Missing cells26123
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 MiB
Average record size in memory136.0 B

Variable types

Numeric11
Text2
Categorical4

Alerts

obtained_date is highly imbalanced (74.6%)Imbalance
track_name has 10676 (26.7%) missing valuesMissing
popularity has 3937 (9.8%) missing valuesMissing
danceability has 2046 (5.1%) missing valuesMissing
key has 1376 (3.4%) missing valuesMissing
mode has 1826 (4.6%) missing valuesMissing
obtained_date has 6262 (15.7%) missing valuesMissing
instance_id is uniformly distributedUniform
instance_id has unique valuesUnique
popularity has 507 (1.3%) zerosZeros
instrumentalness has 12011 (30.0%) zerosZeros

Reproduction

Analysis started2024-03-14 17:20:11.491283
Analysis finished2024-03-14 17:20:30.128973
Duration18.64 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

instance_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26050.117
Minimum1000
Maximum50999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:30.175445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3514.95
Q113554.75
median26079.5
Q338562.25
95-th percentile48493.05
Maximum50999
Range49999
Interquartile range (IQR)25007.5

Descriptive statistics

Standard deviation14441.501
Coefficient of variation (CV)0.55437373
Kurtosis-1.2006713
Mean26050.117
Median Absolute Deviation (MAD)12503
Skewness-0.004805661
Sum1.0420047 × 109
Variance2.0855694 × 108
MonotonicityNot monotonic
2024-03-14T20:20:30.316221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28097 1
 
< 0.1%
3473 1
 
< 0.1%
5453 1
 
< 0.1%
18742 1
 
< 0.1%
7314 1
 
< 0.1%
23964 1
 
< 0.1%
33538 1
 
< 0.1%
44474 1
 
< 0.1%
36120 1
 
< 0.1%
5052 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
ValueCountFrequency (%)
1000 1
< 0.1%
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
ValueCountFrequency (%)
50999 1
< 0.1%
50998 1
< 0.1%
50997 1
< 0.1%
50996 1
< 0.1%
50995 1
< 0.1%
50994 1
< 0.1%
50993 1
< 0.1%
50992 1
< 0.1%
50991 1
< 0.1%
50990 1
< 0.1%

track_name
Text

MISSING 

Distinct25972
Distinct (%)88.6%
Missing10676
Missing (%)26.7%
Memory size312.6 KiB
2024-03-14T20:20:30.637782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length242
Median length150
Mean length20.234757
Min length1

Characters and Unicode

Total characters593364
Distinct characters965
Distinct categories20 ?
Distinct scripts8 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23450 ?
Unique (%)80.0%

Sample

1st rowUber Everywhere
2nd rowLove Is All
3rd rowShould Have Known Better
4th rowCryin'
5th rowUnder The Water
ValueCountFrequency (%)
4174
 
3.8%
the 3222
 
2.9%
in 1941
 
1.7%
feat 1622
 
1.5%
i 1424
 
1.3%
you 1346
 
1.2%
no 1263
 
1.1%
a 1146
 
1.0%
of 1130
 
1.0%
me 1013
 
0.9%
Other values (18411) 92965
83.6%
2024-03-14T20:20:31.046882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81922
 
13.8%
e 50333
 
8.5%
o 35463
 
6.0%
a 32900
 
5.5%
n 29339
 
4.9%
i 29161
 
4.9%
t 26111
 
4.4%
r 25186
 
4.2%
s 17893
 
3.0%
l 17816
 
3.0%
Other values (955) 247240
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365971
61.7%
Uppercase Letter 100288
 
16.9%
Space Separator 81922
 
13.8%
Other Punctuation 17964
 
3.0%
Decimal Number 11449
 
1.9%
Other Letter 4373
 
0.7%
Dash Punctuation 3973
 
0.7%
Close Punctuation 3463
 
0.6%
Open Punctuation 3455
 
0.6%
Modifier Letter 161
 
< 0.1%
Other values (10) 345
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
205
 
4.7%
101
 
2.3%
78
 
1.8%
74
 
1.7%
69
 
1.6%
65
 
1.5%
64
 
1.5%
64
 
1.5%
61
 
1.4%
58
 
1.3%
Other values (750) 3534
80.8%
Lowercase Letter
ValueCountFrequency (%)
e 50333
13.8%
o 35463
 
9.7%
a 32900
 
9.0%
n 29339
 
8.0%
i 29161
 
8.0%
t 26111
 
7.1%
r 25186
 
6.9%
s 17893
 
4.9%
l 17816
 
4.9%
h 13087
 
3.6%
Other values (76) 88682
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 8067
 
8.0%
T 7724
 
7.7%
M 6945
 
6.9%
I 6833
 
6.8%
A 6226
 
6.2%
L 5717
 
5.7%
B 5298
 
5.3%
R 4794
 
4.8%
C 4641
 
4.6%
D 4494
 
4.5%
Other values (33) 39549
39.4%
Other Punctuation
ValueCountFrequency (%)
. 6672
37.1%
, 3367
18.7%
' 2816
15.7%
: 2247
 
12.5%
" 1012
 
5.6%
& 660
 
3.7%
/ 452
 
2.5%
! 335
 
1.9%
? 166
 
0.9%
* 81
 
0.5%
Other values (10) 156
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 2451
21.4%
2 2102
18.4%
0 1613
14.1%
3 912
 
8.0%
4 872
 
7.6%
9 797
 
7.0%
5 745
 
6.5%
6 740
 
6.5%
7 651
 
5.7%
8 566
 
4.9%
Math Symbol
ValueCountFrequency (%)
~ 22
29.7%
| 18
24.3%
+ 15
20.3%
< 6
 
8.1%
= 4
 
5.4%
> 4
 
5.4%
3
 
4.1%
1
 
1.4%
1
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 3295
95.4%
[ 130
 
3.8%
22
 
0.6%
4
 
0.1%
2
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3297
95.2%
] 130
 
3.8%
26
 
0.8%
4
 
0.1%
4
 
0.1%
2
 
0.1%
Other Symbol
ValueCountFrequency (%)
° 3
33.3%
2
22.2%
® 1
 
11.1%
1
 
11.1%
1
 
11.1%
1
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
- 3924
98.8%
31
 
0.8%
10
 
0.3%
8
 
0.2%
Modifier Letter
ValueCountFrequency (%)
152
94.4%
9
 
5.6%
Final Punctuation
ValueCountFrequency (%)
118
92.2%
10
 
7.8%
Initial Punctuation
ValueCountFrequency (%)
11
64.7%
6
35.3%
Format
ValueCountFrequency (%)
8
80.0%
2
 
20.0%
Space Separator
ValueCountFrequency (%)
81922
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 92
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 4
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 466161
78.6%
Common 122722
 
20.7%
Katakana 1677
 
0.3%
Han 1519
 
0.3%
Hiragana 1178
 
0.2%
Cyrillic 98
 
< 0.1%
Hebrew 8
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
20
 
1.3%
19
 
1.3%
17
 
1.1%
16
 
1.1%
13
 
0.9%
13
 
0.9%
13
 
0.9%
13
 
0.9%
12
 
0.8%
調 12
 
0.8%
Other values (597) 1371
90.3%
Latin
ValueCountFrequency (%)
e 50333
 
10.8%
o 35463
 
7.6%
a 32900
 
7.1%
n 29339
 
6.3%
i 29161
 
6.3%
t 26111
 
5.6%
r 25186
 
5.4%
s 17893
 
3.8%
l 17816
 
3.8%
h 13087
 
2.8%
Other values (85) 188872
40.5%
Katakana
ValueCountFrequency (%)
101
 
6.0%
78
 
4.7%
74
 
4.4%
65
 
3.9%
64
 
3.8%
61
 
3.6%
50
 
3.0%
48
 
2.9%
47
 
2.8%
46
 
2.7%
Other values (66) 1043
62.2%
Common
ValueCountFrequency (%)
81922
66.8%
. 6672
 
5.4%
- 3924
 
3.2%
, 3367
 
2.7%
) 3297
 
2.7%
( 3295
 
2.7%
' 2816
 
2.3%
1 2451
 
2.0%
: 2247
 
1.8%
2 2102
 
1.7%
Other values (64) 10629
 
8.7%
Hiragana
ValueCountFrequency (%)
205
 
17.4%
69
 
5.9%
64
 
5.4%
58
 
4.9%
48
 
4.1%
35
 
3.0%
33
 
2.8%
33
 
2.8%
29
 
2.5%
26
 
2.2%
Other values (61) 578
49.1%
Cyrillic
ValueCountFrequency (%)
о 12
 
12.2%
а 11
 
11.2%
е 7
 
7.1%
и 7
 
7.1%
н 5
 
5.1%
с 5
 
5.1%
р 4
 
4.1%
П 3
 
3.1%
ж 3
 
3.1%
я 3
 
3.1%
Other values (24) 38
38.8%
Hebrew
ValueCountFrequency (%)
ל 2
25.0%
ר 1
12.5%
ב 1
12.5%
ק 1
12.5%
י 1
12.5%
ו 1
12.5%
ת 1
12.5%
Greek
ValueCountFrequency (%)
μ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587570
99.0%
Katakana 1869
 
0.3%
CJK 1510
 
0.3%
Hiragana 1178
 
0.2%
None 941
 
0.2%
Punctuation 179
 
< 0.1%
Cyrillic 98
 
< 0.1%
Hebrew 8
 
< 0.1%
Misc Symbols 4
 
< 0.1%
Math Operators 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81922
 
13.9%
e 50333
 
8.6%
o 35463
 
6.0%
a 32900
 
5.6%
n 29339
 
5.0%
i 29161
 
5.0%
t 26111
 
4.4%
r 25186
 
4.3%
s 17893
 
3.0%
l 17816
 
3.0%
Other values (80) 241446
41.1%
None
ValueCountFrequency (%)
é 237
25.2%
ü 69
 
7.3%
ö 66
 
7.0%
è 63
 
6.7%
ó 45
 
4.8%
ä 43
 
4.6%
í 41
 
4.4%
à 36
 
3.8%
31
 
3.3%
á 29
 
3.1%
Other values (51) 281
29.9%
Hiragana
ValueCountFrequency (%)
205
 
17.4%
69
 
5.9%
64
 
5.4%
58
 
4.9%
48
 
4.1%
35
 
3.0%
33
 
2.8%
33
 
2.8%
29
 
2.5%
26
 
2.2%
Other values (61) 578
49.1%
Katakana
ValueCountFrequency (%)
152
 
8.1%
101
 
5.4%
78
 
4.2%
74
 
4.0%
65
 
3.5%
64
 
3.4%
61
 
3.3%
50
 
2.7%
48
 
2.6%
47
 
2.5%
Other values (68) 1129
60.4%
Punctuation
ValueCountFrequency (%)
118
65.9%
11
 
6.1%
10
 
5.6%
10
 
5.6%
8
 
4.5%
8
 
4.5%
6
 
3.4%
5
 
2.8%
2
 
1.1%
1
 
0.6%
CJK
ValueCountFrequency (%)
20
 
1.3%
19
 
1.3%
17
 
1.1%
16
 
1.1%
13
 
0.9%
13
 
0.9%
13
 
0.9%
13
 
0.9%
12
 
0.8%
調 12
 
0.8%
Other values (596) 1362
90.2%
Cyrillic
ValueCountFrequency (%)
о 12
 
12.2%
а 11
 
11.2%
е 7
 
7.1%
и 7
 
7.1%
н 5
 
5.1%
с 5
 
5.1%
р 4
 
4.1%
П 3
 
3.1%
ж 3
 
3.1%
я 3
 
3.1%
Other values (24) 38
38.8%
Math Operators
ValueCountFrequency (%)
3
100.0%
Misc Symbols
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Hebrew
ValueCountFrequency (%)
ל 2
25.0%
ר 1
12.5%
ב 1
12.5%
ק 1
12.5%
י 1
12.5%
ו 1
12.5%
ת 1
12.5%
Number Forms
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
50.0%
1
50.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

MISSING  ZEROS 

Distinct98
Distinct (%)0.3%
Missing3937
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean44.127638
Minimum0
Maximum99
Zeros507
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:31.209205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q134
median45
Q355
95-th percentile68
Maximum99
Range99
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.560746
Coefficient of variation (CV)0.35263038
Kurtosis0.018666012
Mean44.127638
Median Absolute Deviation (MAD)11
Skewness-0.30713919
Sum1591375
Variance242.13681
MonotonicityNot monotonic
2024-03-14T20:20:31.322948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 942
 
2.4%
54 925
 
2.3%
50 922
 
2.3%
55 920
 
2.3%
53 897
 
2.2%
51 893
 
2.2%
38 879
 
2.2%
36 872
 
2.2%
56 871
 
2.2%
37 857
 
2.1%
Other values (88) 27085
67.7%
(Missing) 3937
 
9.8%
ValueCountFrequency (%)
0 507
1.3%
1 26
 
0.1%
2 41
 
0.1%
3 32
 
0.1%
4 31
 
0.1%
5 19
 
< 0.1%
6 11
 
< 0.1%
7 15
 
< 0.1%
8 39
 
0.1%
9 39
 
0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
93 2
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
90 5
< 0.1%
89 6
< 0.1%
88 4
< 0.1%

acousticness
Real number (ℝ)

Distinct4063
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30576827
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:31.432129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00039
Q10.02
median0.143
Q30.551
95-th percentile0.978
Maximum0.996
Range0.996
Interquartile range (IQR)0.531

Descriptive statistics

Standard deviation0.3414694
Coefficient of variation (CV)1.1167587
Kurtosis-0.71805981
Mean0.30576827
Median Absolute Deviation (MAD)0.14012
Skewness0.88629725
Sum12230.731
Variance0.11660135
MonotonicityNot monotonic
2024-03-14T20:20:31.573957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 218
 
0.5%
0.994 192
 
0.5%
0.993 164
 
0.4%
0.992 161
 
0.4%
0.99 125
 
0.3%
0.991 120
 
0.3%
0.989 103
 
0.3%
0.985 95
 
0.2%
0.982 92
 
0.2%
0.987 91
 
0.2%
Other values (4053) 38639
96.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.27 × 10-61
 
< 0.1%
1.37 × 10-61
 
< 0.1%
1.38 × 10-61
 
< 0.1%
1.39 × 10-63
< 0.1%
1.46 × 10-61
 
< 0.1%
1.55 × 10-61
 
< 0.1%
1.6 × 10-61
 
< 0.1%
1.62 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 70
 
0.2%
0.995 218
0.5%
0.994 192
0.5%
0.993 164
0.4%
0.992 161
0.4%
0.991 120
0.3%
0.99 125
0.3%
0.989 103
0.3%
0.988 87
 
0.2%
0.987 91
0.2%

danceability
Real number (ℝ)

MISSING 

Distinct1046
Distinct (%)2.8%
Missing2046
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean0.55823759
Minimum0.0596
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:31.706252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0596
5-th percentile0.235
Q10.442
median0.568
Q30.687
95-th percentile0.838
Maximum0.986
Range0.9264
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.1783688
Coefficient of variation (CV)0.3195213
Kurtosis-0.30556288
Mean0.55823759
Median Absolute Deviation (MAD)0.122
Skewness-0.29036758
Sum21187.349
Variance0.031815428
MonotonicityNot monotonic
2024-03-14T20:20:32.000470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.529 114
 
0.3%
0.589 104
 
0.3%
0.61 103
 
0.3%
0.499 101
 
0.3%
0.539 100
 
0.2%
0.554 100
 
0.2%
0.657 100
 
0.2%
0.576 100
 
0.2%
0.628 99
 
0.2%
0.54 98
 
0.2%
Other values (1036) 36935
92.3%
(Missing) 2046
 
5.1%
ValueCountFrequency (%)
0.0596 1
< 0.1%
0.06 1
< 0.1%
0.0606 1
< 0.1%
0.0607 2
< 0.1%
0.061 1
< 0.1%
0.0613 1
< 0.1%
0.0614 1
< 0.1%
0.0616 1
< 0.1%
0.0617 1
< 0.1%
0.0618 1
< 0.1%
ValueCountFrequency (%)
0.986 1
 
< 0.1%
0.98 2
< 0.1%
0.979 1
 
< 0.1%
0.977 1
 
< 0.1%
0.975 1
 
< 0.1%
0.973 1
 
< 0.1%
0.972 1
 
< 0.1%
0.971 3
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%

duration_ms
Real number (ℝ)

Distinct22457
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221080.78
Minimum-1
Maximum4830606
Zeros0
Zeros (%)0.0%
Negative3953
Negative (%)9.9%
Memory size312.6 KiB
2024-03-14T20:20:32.163774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1174793.75
median219142
Q3268480
95-th percentile401000
Maximum4830606
Range4830607
Interquartile range (IQR)93686.25

Descriptive statistics

Standard deviation129611.78
Coefficient of variation (CV)0.58626437
Kurtosis115.46389
Mean221080.78
Median Absolute Deviation (MAD)46765
Skewness4.7786172
Sum8.843231 × 109
Variance1.6799214 × 1010
MonotonicityNot monotonic
2024-03-14T20:20:32.311106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 3953
 
9.9%
192000 25
 
0.1%
180000 25
 
0.1%
240000 24
 
0.1%
208000 17
 
< 0.1%
216000 15
 
< 0.1%
200000 15
 
< 0.1%
210000 15
 
< 0.1%
182400 14
 
< 0.1%
270000 13
 
< 0.1%
Other values (22447) 35884
89.7%
ValueCountFrequency (%)
-1 3953
9.9%
15509 1
 
< 0.1%
16316 1
 
< 0.1%
19693 1
 
< 0.1%
20096 1
 
< 0.1%
22750 1
 
< 0.1%
26293 1
 
< 0.1%
28947 1
 
< 0.1%
30027 1
 
< 0.1%
30097 1
 
< 0.1%
ValueCountFrequency (%)
4830606 1
< 0.1%
4497994 1
< 0.1%
4276000 1
< 0.1%
3195440 1
< 0.1%
2764934 1
< 0.1%
2019293 1
< 0.1%
1935787 1
< 0.1%
1872653 1
< 0.1%
1778440 1
< 0.1%
1617333 1
< 0.1%

energy
Real number (ℝ)

Distinct1990
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60028519
Minimum0.000792
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:32.439757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.000792
5-th percentile0.073495
Q10.432
median0.644
Q30.817
95-th percentile0.953
Maximum0.999
Range0.998208
Interquartile range (IQR)0.385

Descriptive statistics

Standard deviation0.26454414
Coefficient of variation (CV)0.44069743
Kurtosis-0.59484455
Mean0.60028519
Median Absolute Deviation (MAD)0.188
Skewness-0.5716041
Sum24011.407
Variance0.0699836
MonotonicityNot monotonic
2024-03-14T20:20:32.557226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.859 84
 
0.2%
0.675 81
 
0.2%
0.805 79
 
0.2%
0.72 79
 
0.2%
0.833 77
 
0.2%
0.978 76
 
0.2%
0.714 76
 
0.2%
0.545 75
 
0.2%
0.83 75
 
0.2%
0.625 74
 
0.2%
Other values (1980) 39224
98.1%
ValueCountFrequency (%)
0.000792 1
< 0.1%
0.000795 1
< 0.1%
0.000825 1
< 0.1%
0.0009 1
< 0.1%
0.00101 1
< 0.1%
0.00104 1
< 0.1%
0.00106 2
< 0.1%
0.00108 1
< 0.1%
0.00117 1
< 0.1%
0.00123 1
< 0.1%
ValueCountFrequency (%)
0.999 4
 
< 0.1%
0.998 14
 
< 0.1%
0.997 14
 
< 0.1%
0.996 25
0.1%
0.995 29
0.1%
0.994 27
0.1%
0.993 25
0.1%
0.992 33
0.1%
0.991 36
0.1%
0.99 35
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4970
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18179051
Minimum0
Maximum0.994
Zeros12011
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:32.684851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00015
Q30.155
95-th percentile0.908
Maximum0.994
Range0.994
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation0.32541658
Coefficient of variation (CV)1.7900637
Kurtosis0.44817693
Mean0.18179051
Median Absolute Deviation (MAD)0.00015
Skewness1.4839272
Sum7271.6203
Variance0.10589595
MonotonicityNot monotonic
2024-03-14T20:20:32.800725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12011
30.0%
0.891 55
 
0.1%
0.897 54
 
0.1%
0.898 54
 
0.1%
0.914 53
 
0.1%
0.902 52
 
0.1%
0.912 52
 
0.1%
0.917 49
 
0.1%
0.908 49
 
0.1%
0.934 48
 
0.1%
Other values (4960) 27523
68.8%
ValueCountFrequency (%)
0 12011
30.0%
1 × 10-63
 
< 0.1%
1.01 × 10-623
 
0.1%
1.02 × 10-615
 
< 0.1%
1.03 × 10-613
 
< 0.1%
1.04 × 10-615
 
< 0.1%
1.05 × 10-617
 
< 0.1%
1.06 × 10-612
 
< 0.1%
1.07 × 10-614
 
< 0.1%
1.08 × 10-614
 
< 0.1%
ValueCountFrequency (%)
0.994 1
 
< 0.1%
0.993 2
 
< 0.1%
0.992 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 3
< 0.1%
0.987 2
 
< 0.1%
0.986 3
< 0.1%
0.985 5
< 0.1%
0.984 5
< 0.1%
0.983 4
< 0.1%

key
Categorical

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing1376
Missing (%)3.4%
Memory size312.6 KiB
G
4454 
C
4253 
C#
4178 
D
4123 
A
3739 
Other values (7)
17877 

Length

Max length2
Median length1
Mean length1.3365783
Min length1

Characters and Unicode

Total characters51624
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF#
2nd rowC#
3rd rowD
4th rowE
5th rowG

Common Values

ValueCountFrequency (%)
G 4454
11.1%
C 4253
10.6%
C# 4178
10.4%
D 4123
10.3%
A 3739
9.3%
F 3283
8.2%
B 2889
7.2%
E 2883
7.2%
A# 2641
6.6%
G# 2536
6.3%
Other values (2) 3645
9.1%

Length

2024-03-14T20:20:32.921387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 8431
21.8%
g 6990
18.1%
a 6380
16.5%
f 5711
14.8%
d 5340
13.8%
b 2889
 
7.5%
e 2883
 
7.5%

Most occurring characters

ValueCountFrequency (%)
# 13000
25.2%
C 8431
16.3%
G 6990
13.5%
A 6380
12.4%
F 5711
11.1%
D 5340
10.3%
B 2889
 
5.6%
E 2883
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38624
74.8%
Other Punctuation 13000
 
25.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8431
21.8%
G 6990
18.1%
A 6380
16.5%
F 5711
14.8%
D 5340
13.8%
B 2889
 
7.5%
E 2883
 
7.5%
Other Punctuation
ValueCountFrequency (%)
# 13000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38624
74.8%
Common 13000
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8431
21.8%
G 6990
18.1%
A 6380
16.5%
F 5711
14.8%
D 5340
13.8%
B 2889
 
7.5%
E 2883
 
7.5%
Common
ValueCountFrequency (%)
# 13000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 13000
25.2%
C 8431
16.3%
G 6990
13.5%
A 6380
12.4%
F 5711
11.1%
D 5340
10.3%
B 2889
 
5.6%
E 2883
 
5.6%

liveness
Real number (ℝ)

Distinct1627
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19380937
Minimum0.00967
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:33.042726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0625
Q10.0969
median0.127
Q30.24325
95-th percentile0.548
Maximum1
Range0.99033
Interquartile range (IQR)0.14635

Descriptive statistics

Standard deviation0.16088587
Coefficient of variation (CV)0.83012429
Kurtosis5.6454779
Mean0.19380937
Median Absolute Deviation (MAD)0.0446
Skewness2.236441
Sum7752.375
Variance0.025884263
MonotonicityNot monotonic
2024-03-14T20:20:33.178523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 501
 
1.3%
0.111 487
 
1.2%
0.108 477
 
1.2%
0.109 442
 
1.1%
0.107 422
 
1.1%
0.112 421
 
1.1%
0.105 401
 
1.0%
0.104 399
 
1.0%
0.106 389
 
1.0%
0.103 382
 
1.0%
Other values (1617) 35679
89.2%
ValueCountFrequency (%)
0.00967 1
< 0.1%
0.0169 1
< 0.1%
0.0173 1
< 0.1%
0.0188 1
< 0.1%
0.0191 1
< 0.1%
0.0194 1
< 0.1%
0.0204 1
< 0.1%
0.0208 1
< 0.1%
0.0209 1
< 0.1%
0.0212 2
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.996 1
< 0.1%
0.993 1
< 0.1%
0.992 1
< 0.1%
0.991 1
< 0.1%
0.99 1
< 0.1%
0.989 1
< 0.1%
0.988 2
< 0.1%
0.987 2
< 0.1%
0.986 2
< 0.1%

loudness
Real number (ℝ)

Distinct16803
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.667314
Minimum-108.358
Maximum1.949
Zeros0
Zeros (%)0.0%
Negative39961
Negative (%)99.9%
Memory size312.6 KiB
2024-03-14T20:20:33.319230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-108.358
5-th percentile-29.80105
Q1-11.525
median-7.456
Q3-5.246
95-th percentile-3.0979
Maximum1.949
Range110.307
Interquartile range (IQR)6.279

Descriptive statistics

Standard deviation14.509358
Coefficient of variation (CV)-1.2435903
Kurtosis15.401729
Mean-11.667314
Median Absolute Deviation (MAD)2.713
Skewness-3.8320738
Sum-466692.56
Variance210.52148
MonotonicityNot monotonic
2024-03-14T20:20:33.431690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.133 15
 
< 0.1%
-3.665 14
 
< 0.1%
-5.606 14
 
< 0.1%
-7.066 13
 
< 0.1%
-4.566 13
 
< 0.1%
-5.982 13
 
< 0.1%
-5.443 13
 
< 0.1%
-5.659 13
 
< 0.1%
-6.217 13
 
< 0.1%
-7.321 12
 
< 0.1%
Other values (16793) 39867
99.7%
ValueCountFrequency (%)
-108.358 1
< 0.1%
-107.986 1
< 0.1%
-106.758 1
< 0.1%
-106.111 1
< 0.1%
-105.191 1
< 0.1%
-105.112 1
< 0.1%
-105.09 1
< 0.1%
-104.741 1
< 0.1%
-104.34 1
< 0.1%
-104.267 1
< 0.1%
ValueCountFrequency (%)
1.949 1
< 0.1%
1.61 1
< 0.1%
1.585 1
< 0.1%
1.342 1
< 0.1%
1.314 1
< 0.1%
1.275 1
< 0.1%
1.012 1
< 0.1%
0.954 1
< 0.1%
0.899 1
< 0.1%
0.892 1
< 0.1%

mode
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1826
Missing (%)4.6%
Memory size312.6 KiB
Major
24553 
Minor
13621 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters190870
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinor
2nd rowMajor
3rd rowMinor
4th rowMinor
5th rowMajor

Common Values

ValueCountFrequency (%)
Major 24553
61.4%
Minor 13621
34.1%
(Missing) 1826
 
4.6%

Length

2024-03-14T20:20:33.549438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:20:33.628589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
major 24553
64.3%
minor 13621
35.7%

Most occurring characters

ValueCountFrequency (%)
M 38174
20.0%
o 38174
20.0%
r 38174
20.0%
a 24553
12.9%
j 24553
12.9%
i 13621
 
7.1%
n 13621
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 152696
80.0%
Uppercase Letter 38174
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 38174
25.0%
r 38174
25.0%
a 24553
16.1%
j 24553
16.1%
i 13621
 
8.9%
n 13621
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
M 38174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 38174
20.0%
o 38174
20.0%
r 38174
20.0%
a 24553
12.9%
j 24553
12.9%
i 13621
 
7.1%
n 13621
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 38174
20.0%
o 38174
20.0%
r 38174
20.0%
a 24553
12.9%
j 24553
12.9%
i 13621
 
7.1%
n 13621
 
7.1%

speechiness
Real number (ℝ)

Distinct2019
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1298754
Minimum0.0223
Maximum1.889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:33.697401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0223
5-th percentile0.0282
Q10.0364
median0.0504
Q30.114
95-th percentile0.438
Maximum1.889
Range1.8667
Interquartile range (IQR)0.0776

Descriptive statistics

Standard deviation0.21232388
Coefficient of variation (CV)1.6348274
Kurtosis13.870344
Mean0.1298754
Median Absolute Deviation (MAD)0.0187
Skewness3.6344209
Sum5195.0162
Variance0.045081428
MonotonicityNot monotonic
2024-03-14T20:20:33.847960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0332 140
 
0.4%
0.0337 125
 
0.3%
0.0315 124
 
0.3%
0.0329 123
 
0.3%
0.0358 120
 
0.3%
0.0364 112
 
0.3%
0.0291 112
 
0.3%
0.0356 111
 
0.3%
0.0343 111
 
0.3%
0.0323 111
 
0.3%
Other values (2009) 38811
97.0%
ValueCountFrequency (%)
0.0223 1
 
< 0.1%
0.0224 3
< 0.1%
0.0225 1
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 1
 
< 0.1%
0.0228 6
< 0.1%
0.0229 2
 
< 0.1%
0.023 1
 
< 0.1%
0.0231 5
< 0.1%
0.0232 3
< 0.1%
ValueCountFrequency (%)
1.889 1
< 0.1%
1.739 1
< 0.1%
1.642 1
< 0.1%
1.623 1
< 0.1%
1.622 1
< 0.1%
1.576 1
< 0.1%
1.515 1
< 0.1%
1.504 1
< 0.1%
1.5 1
< 0.1%
1.491 1
< 0.1%

tempo
Text

Distinct25010
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:34.088332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length8.39585
Min length1

Characters and Unicode

Total characters335834
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18454 ?
Unique (%)46.1%

Sample

1st row167.357
2nd row140.181
3rd row?
4th row120.052
5th row90.97200000000001
ValueCountFrequency (%)
3996
 
10.0%
100.00299999999999 15
 
< 0.1%
130.016 14
 
< 0.1%
140.011 13
 
< 0.1%
140.007 13
 
< 0.1%
120.01899999999999 12
 
< 0.1%
130.04 12
 
< 0.1%
119.985 12
 
< 0.1%
100.014 12
 
< 0.1%
129.984 11
 
< 0.1%
Other values (25000) 35890
89.7%
2024-03-14T20:20:34.428655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 72983
21.7%
0 63881
19.0%
1 44363
13.2%
. 36004
10.7%
2 19323
 
5.8%
7 17907
 
5.3%
8 17875
 
5.3%
5 15256
 
4.5%
4 15189
 
4.5%
3 15156
 
4.5%
Other values (2) 17897
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 295834
88.1%
Other Punctuation 40000
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 72983
24.7%
0 63881
21.6%
1 44363
15.0%
2 19323
 
6.5%
7 17907
 
6.1%
8 17875
 
6.0%
5 15256
 
5.2%
4 15189
 
5.1%
3 15156
 
5.1%
6 13901
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 36004
90.0%
? 3996
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 335834
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 72983
21.7%
0 63881
19.0%
1 44363
13.2%
. 36004
10.7%
2 19323
 
5.8%
7 17907
 
5.3%
8 17875
 
5.3%
5 15256
 
4.5%
4 15189
 
4.5%
3 15156
 
4.5%
Other values (2) 17897
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 72983
21.7%
0 63881
19.0%
1 44363
13.2%
. 36004
10.7%
2 19323
 
5.8%
7 17907
 
5.3%
8 17875
 
5.3%
5 15256
 
4.5%
4 15189
 
4.5%
3 15156
 
4.5%
Other values (2) 17897
 
5.3%

obtained_date
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6262
Missing (%)15.7%
Memory size312.6 KiB
4-Apr
30192 
3-Apr
 
2718
5-Apr
 
553
1-Apr
 
274
0/4
 
1

Length

Max length5
Median length5
Mean length4.9999407
Min length3

Characters and Unicode

Total characters168688
Distinct characters10
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4-Apr
2nd row4-Apr
3rd row4-Apr
4th row4-Apr
5th row4-Apr

Common Values

ValueCountFrequency (%)
4-Apr 30192
75.5%
3-Apr 2718
 
6.8%
5-Apr 553
 
1.4%
1-Apr 274
 
0.7%
0/4 1
 
< 0.1%
(Missing) 6262
 
15.7%

Length

2024-03-14T20:20:34.554049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:20:34.681085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4-apr 30192
89.5%
3-apr 2718
 
8.1%
5-apr 553
 
1.6%
1-apr 274
 
0.8%
0/4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
- 33737
20.0%
A 33737
20.0%
p 33737
20.0%
r 33737
20.0%
4 30193
17.9%
3 2718
 
1.6%
5 553
 
0.3%
1 274
 
0.2%
0 1
 
< 0.1%
/ 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67474
40.0%
Decimal Number 33739
20.0%
Dash Punctuation 33737
20.0%
Uppercase Letter 33737
20.0%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 30193
89.5%
3 2718
 
8.1%
5 553
 
1.6%
1 274
 
0.8%
0 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
p 33737
50.0%
r 33737
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 33737
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 33737
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101211
60.0%
Common 67477
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 33737
50.0%
4 30193
44.7%
3 2718
 
4.0%
5 553
 
0.8%
1 274
 
0.4%
0 1
 
< 0.1%
/ 1
 
< 0.1%
Latin
ValueCountFrequency (%)
A 33737
33.3%
p 33737
33.3%
r 33737
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 33737
20.0%
A 33737
20.0%
p 33737
20.0%
r 33737
20.0%
4 30193
17.9%
3 2718
 
1.6%
5 553
 
0.3%
1 274
 
0.2%
0 1
 
< 0.1%
/ 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1594
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45709956
Minimum0
Maximum0.992
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2024-03-14T20:20:34.836100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0669
Q10.257
median0.4505
Q30.649
95-th percentile0.877
Maximum0.992
Range0.992
Interquartile range (IQR)0.392

Descriptive statistics

Standard deviation0.24712314
Coefficient of variation (CV)0.54063307
Kurtosis-0.9321704
Mean0.45709956
Median Absolute Deviation (MAD)0.1955
Skewness0.12586194
Sum18283.982
Variance0.061069846
MonotonicityNot monotonic
2024-03-14T20:20:34.971583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.338 85
 
0.2%
0.332 74
 
0.2%
0.362 72
 
0.2%
0.347 71
 
0.2%
0.474 71
 
0.2%
0.324 69
 
0.2%
0.351 69
 
0.2%
0.569 68
 
0.2%
0.358 68
 
0.2%
0.354 68
 
0.2%
Other values (1584) 39285
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.0193 1
< 0.1%
0.0241 1
< 0.1%
0.0246 1
< 0.1%
0.0247 1
< 0.1%
0.0251 1
< 0.1%
0.0252 1
< 0.1%
0.0262 1
< 0.1%
0.0264 2
< 0.1%
0.0266 1
< 0.1%
ValueCountFrequency (%)
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.987 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 4
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 3
< 0.1%
0.98 3
< 0.1%
0.979 2
< 0.1%

music_genre
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Alternative
4059 
Jazz
4055 
Electronic
4020 
Anime
4017 
Blues
3987 
Other values (5)
19862 

Length

Max length11
Median length9
Mean length6.507075
Min length3

Characters and Unicode

Total characters260283
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCountry
2nd rowRap
3rd rowJazz
4th rowHip-Hop
5th rowRock

Common Values

ValueCountFrequency (%)
Alternative 4059
10.1%
Jazz 4055
10.1%
Electronic 4020
10.1%
Anime 4017
10.0%
Blues 3987
10.0%
Country 3985
10.0%
Rap 3980
10.0%
Classical 3980
10.0%
Rock 3960
9.9%
Hip-Hop 3957
9.9%

Length

2024-03-14T20:20:35.130936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:20:35.290731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
alternative 4059
10.1%
jazz 4055
10.1%
electronic 4020
10.1%
anime 4017
10.0%
blues 3987
10.0%
country 3985
10.0%
rap 3980
10.0%
classical 3980
10.0%
rock 3960
9.9%
hip-hop 3957
9.9%

Most occurring characters

ValueCountFrequency (%)
e 20142
 
7.7%
a 20054
 
7.7%
i 20033
 
7.7%
l 20026
 
7.7%
t 16123
 
6.2%
n 16081
 
6.2%
c 15980
 
6.1%
o 15922
 
6.1%
r 12064
 
4.6%
s 11947
 
4.6%
Other values (15) 91911
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212369
81.6%
Uppercase Letter 43957
 
16.9%
Dash Punctuation 3957
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20142
9.5%
a 20054
9.4%
i 20033
9.4%
l 20026
9.4%
t 16123
 
7.6%
n 16081
 
7.6%
c 15980
 
7.5%
o 15922
 
7.5%
r 12064
 
5.7%
s 11947
 
5.6%
Other values (7) 43997
20.7%
Uppercase Letter
ValueCountFrequency (%)
A 8076
18.4%
C 7965
18.1%
R 7940
18.1%
H 7914
18.0%
J 4055
9.2%
E 4020
9.1%
B 3987
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 3957
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256326
98.5%
Common 3957
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20142
 
7.9%
a 20054
 
7.8%
i 20033
 
7.8%
l 20026
 
7.8%
t 16123
 
6.3%
n 16081
 
6.3%
c 15980
 
6.2%
o 15922
 
6.2%
r 12064
 
4.7%
s 11947
 
4.7%
Other values (14) 87954
34.3%
Common
ValueCountFrequency (%)
- 3957
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20142
 
7.7%
a 20054
 
7.7%
i 20033
 
7.7%
l 20026
 
7.7%
t 16123
 
6.2%
n 16081
 
6.2%
c 15980
 
6.1%
o 15922
 
6.1%
r 12064
 
4.6%
s 11947
 
4.6%
Other values (15) 91911
35.3%

Interactions

2024-03-14T20:20:27.999369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:12.969348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.445933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.862590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.358156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:19.380308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:20.805387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.274813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:23.732280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:25.097070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.499999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:28.126281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.127767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.571005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:17.474297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:19.514826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:20.914164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.370691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:23.868201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:25.192319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.620384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:28.266644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.235200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.706635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:16.183719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.627686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:19.623626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.022814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.522416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.042930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:25.471654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:28.407738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:14.837067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:16.309252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.743546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:19.785228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.165253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.658945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:26.891278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:28.557900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.478280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.952031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:16.435732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.886545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:19.920716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.293037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.758143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.296928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:25.768112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.015443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:28.679783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.588723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.109646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:16.532312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:18.015598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:20.035908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.467232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.898953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.408496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:25.866860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.148181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:28.791223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.688312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.231539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:28.915793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.799184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.344383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:24.638280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.062733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.412281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:29.030376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:13.970620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.442439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:16.958323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2024-03-14T20:20:20.404330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.871183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:23.307304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.734261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.174954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.567807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:29.154964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.140717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.588391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.123073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:18.582500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:20.529728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:21.991650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:23.431898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.856362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.289601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.692232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:29.267425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:14.294263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:15.749656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:17.223987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:18.704572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:20.669690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:22.152002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:23.592045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:24.978733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:26.412501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-14T20:20:27.828849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Missing values

2024-03-14T20:20:29.429582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:20:29.732177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T20:20:30.019026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

instance_idtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
028097NaN38.00.045500NaN152427.00.6360.020200F#0.1110-9.109Minor0.0591167.3574-Apr0.597Country
149793Uber Everywhere67.00.0542000.779-1.00.4550.000000C#0.1770-15.025Major0.2800140.1814-Apr0.197Rap
241545Love Is All37.00.5830000.660311008.00.6500.056200D0.3980-7.738Minor0.0288?NaN0.692Jazz
323453NaN69.00.0122000.680200000.00.5970.000599E0.1140-7.198Minor0.0304120.0524-Apr0.300Hip-Hop
433933Should Have Known Better64.00.9790000.571307698.00.1720.275000G0.1240-20.700Major0.031890.972000000000014-Apr0.297Rock
53872NaN68.00.0024200.585237040.00.8420.006860A0.0866-75.883NaN0.0556118.2114-Apr0.428Rock
637110Cryin'70.00.0002020.445308333.00.8560.010100A0.3670-3.674Major0.0327105.872000000000013-Apr0.486Rock
747456Under The Water51.00.0539000.611203915.00.6440.000000NaN0.0802-5.394Major0.0750144.0414-Apr0.440Rock
822862Chanel Junkie (feat. Future)50.00.0508000.842193732.00.6760.000000B0.0732-4.368Minor0.2240140.0474-Apr0.464Hip-Hop
943030Blackbird49.00.0377000.720567459.00.7910.730000G0.5040-6.902Minor0.036195.014-Apr0.535Jazz
instance_idtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
3999011860Ali Bomaye66.00.02030.378372707.00.69100.000000A#0.4000-5.308Minor0.461075.161999999999994-Apr0.226Rap
3999146479Agony55.00.94200.578214173.00.16600.006900E0.1030-17.194Major0.033599.8914-Apr0.318Rap
399927823NaNNaN0.0304NaN243100.00.94400.000002F#0.0948-1.669Minor0.0613114.887NaN0.549Anime
3999334030NaN46.00.05160.754220467.00.68400.000053E0.1070-10.377Major0.0291116.9554-Apr0.775Country
3999423298Take Care of MommaNaN0.56600.837316360.00.24400.000002G0.1000-13.606Minor0.0750105.98100000000001NaN0.665Blues
3999520742Violin Concerto in G Minor, RV 315, "Summer" from "The Four Seasons": III. Presto. Tempo impettuoso d'estate36.00.92900.306157994.00.34100.913000C0.1620-16.957Minor0.0359151.3753-Apr0.383Classical
3999640095Hey There47.00.86100.649335320.00.09690.452000C0.0955-19.541NaN0.0434112.0384-Apr0.306Jazz
3999721931NaN0.00.05680.475219880.00.96500.000000C#0.1370-2.877Major0.0462171.8824-Apr0.868Country
3999830480Dna Rhapsody14.00.03170.480255627.00.94300.000018A0.3450-0.764Minor0.2370174.0624-Apr0.705Anime
3999918781Objects in the MirrorNaN0.6230NaN259144.00.65800.000019C#0.1070-6.625Major0.053277.0214-Apr0.190Hip-Hop